Selected IQANOVA papers and platform papers supporting AI-QSP, SBML model standardisation, surrogate validation and EHMN model access.
A practical, context-of-use-driven review for validating AI-QSP surrogates across endpoint accuracy, trajectory fidelity, uncertainty, biological plausibility, distributional agreement and computational efficiency.
Read via DOI →A thermodynamically refined, SBML-standardised human metabolic network for genome-scale analysis and QSP integration, designed for reproducibility, interoperability and multi-layer modelling.
Read article →A license-aware AI-QSP integration framework linking EHMN2026 with TRANSFAC, TRANSPATH and HumanPSD for diagnostic-metabolite interpretation and controlled model-building workflows.
Read article →Provides the thermodynamically refined, SBML-standardised metabolic backbone for genome-scale and QSP integration.
Adds a license-aware diagnostic-metabolite interpretation layer linking metabolic genes to regulatory, signalling and translational context.
Defines validation expectations for accelerated surrogates used in dynamic, decision-relevant QSP workflows.
Turns models and workflows into versioned website assets, reports, CSV/JSON outputs and controlled access layers.